Display Settings

As before, the result has been measured with the feature/analytics branch of the v7fasttrack open source distribution, and it will soon be available as a preconfigured Amazon EC2 image. The updated benchmarks AMI with this version of the software will be out there within the next week, to be announced on this blog.

On the Cost of RDF Query Optimization

RDF query optimization is harder than the relational equivalent; first, because there are more joins, hence an NP complete explosion of plan search space, and second, because cardinality estimation is harder and usually less reliable. The work on characteristic sets, pioneered by Thomas Neumann in RDF3X, uses regularities in structure for treating properties usually occurring in the same subject as columns of a table. The same idea is applied for tuning physical representation in the joint Virtuoso / MonetDB work published at WWW 2015.

The Virtuoso results discussed here, however, are all based on a single RDF quad table with Virtuoso's default index configuration.

Introducing query plan caching raises the Virtuoso score from 80 qps to 144 qps at the 256 Mtriple scale. The SPB queries are not extremely complex; lookups with many more triple patterns exist in actual workloads, e.g., Open PHACTS. In such applications, query optimization indeed dominates execution times. In SPB, data volumes touched by queries grow near linearly with data scale. At the 256 Mtriple scale, nearly half of CPU cycles are spent deciding a query plan. Below are the CPU cycles for execution and compilation per query type, sorted by descending sum of the times, scaled to milliseconds per execution. These are taken from a one minute sample of running at full throughput.

We measure the compile and execute times, with and without using hash join. When considering hash join, the throughput is 80 qps. When not considering hash join, the throughput is 110 qps. With query plan caching, the throughput is 145 qps whether or not hash join is considered. Using hash join is not significant for the workload but considering its use in query optimization leads to significant extra work.

With hash join

Compile

Execute

Total

Query

3156 ms

1181 ms

4337 ms

Total

1327 ms

28 ms

1355 ms

query 01

444 ms

460 ms

904 ms

query 08

466 ms

54 ms

520 ms

query 06

123 ms

268 ms

391 ms

query 05

257 ms

5 ms

262 ms

query 11

191 ms

59 ms

250 ms

query 10

9 ms

179 ms

188 ms

query 04

114 ms

26 ms

140 ms

query 07

46 ms

62 ms

108 ms

query 09

71 ms

25 ms

96 ms

query 12

61 ms

13 ms

74 ms

query 03

47 ms

2 ms

49 ms

query 02

Without hash join

Compile

Execute

Total

Query

1816 ms

1019 ms

2835 ms

Total

197 ms

466 ms

663 ms

query 08

609 ms

32 ms

641 ms

query 01

188 ms

293 ms

481 ms

query 05

275 ms

61 ms

336 ms

query 09

163 ms

10 ms

173 ms

query 03

128 ms

38 ms

166 ms

query 10

102 ms

5 ms

107 ms

query 11

63 ms

27 ms

90 ms

query 12

24 ms

57 ms

81 ms

query 06

47 ms

1 ms

48 ms

query 02

15 ms

24 ms

39 ms

query 07

5 ms

5 ms

10 ms

query 04

Considering hash join always slows down compilation, and sometimes improves and sometimes worsens execution. Some improvement in cost-model and plan-space traversal-order is possible, but altogether removing compilation via caching is better still. The results are as expected, since a lookup workload such as SPB has little use for hash join by nature.

The rationale for considering hash join in the first place is that analytical workloads rely heavily on this. A good TPC-H score is simply unfeasible without this as previously discussed on this blog. If RDF is to be a serious contender beyond serving lookups, then hash join is indispensable. The decision for using this however depends on accurate cardinality estimates on either side of the join.

Previous work (e.g., papers from FORTH around MonetDB) advocates doing away with a cost model altogether, since one is hard and unreliable with RDF anyway. The idea is not without its attraction but will lead to missing out of analytics or to relying on query hints for hash join.

The present Virtuoso thinking is that going to rule based optimization is not the preferred solution, but rather using characteristic sets for reducing triples into wider tables, which also cuts down on plan search space and increases reliability of cost estimation.

When looking at execution alone, we see that actual database operations are low in the profile, with memory management taking the top 19%. This is due to CONSTRUCT queries allocating small blocks for returning graphs, which is entirely avoidable.

In this article we will look at Virtuoso vs. Impala with 100G TPC-H on two R3.8 EC2 instances. We get a single user win for Virtuoso by a factor of 136, and a five user win by a factor of 55. The details and analysis follow.

The load setup is the same as ever, with copying from CSV files attached as external tables into Parquet tables. We get lineitem split over 88 Parquet files, which should provide enough parallelism for the platform. The Impala documentation states that there can be up to one thread per file, and here we wish to see maximum parallelism for a single query stream. We use the schema from the Impala github checkout, with string for string and date columns, and decimal for numbers. We suppose the authors know what works best.

The execution behavior is surprising. Sometimes we get full platform utilization, but quite often only 200% CPU per box. The query plan for Q1, for example, says 2 cores per box. This makes no sense, as the same plan fully well knows the table cardinality. The settings for scanner threads and cores to use (in impala-shell) can be changed, but the behavior does not seem to change.

Following are the run times for one query stream.

Query

Virtuoso

Impala

Notes

—

332 s

841 s

Data Load

Q1

1.098 s

164.61 s

Q2

0.187 s

24.19 s

Q3

0.761 s

105.70 s

Q4

0.205 s

179.67 s

Q5

0.808 s

84.51 s

Q6

2.403 s

4.43 s

Q7

0.59 s

270.88 s

Q8

0.775 s

51.89 s

Q9

1.836 s

177.72 s

Q10

3.165 s

39.85 s

Q11

1.37 s

22.56 s

Q12

0.356 s

17.03 s

Q13

2.233 s

103.67 s

Q14

0.488 s

10.86 s

Q15

0.72 s

11.49 s

Q16

0.814 s

23.93 s

Q17

0.681 s

276.06 s

Q18

1.324 s

267.13 s

Q19

0.417 s

368.80 s

Q20

0.792 s

60.45 s

Q21

0.720 s

418.09 s

Q22

0.155 s

40.59 s

Total

20 s

2724 s

Because the platform utilization was often low, we made a second experiment running the same queries in five parallel sessions. We show the average execution time for each query. We then compare this with the Virtuoso throughput run average times. We permute the single query stream used in the first tests in 5 different orders, as per the TPC-H spec. The results are not entirely comparable, because Virtuoso is doing the refreshes in parallel. According to Impala documentation, there is no random delete operation, so the refreshes cannot be implemented.

Just to establish a baseline, we do SELECT COUNT (*) FROM lineitem. This takes 20s when run by itself. When run in five parallel sessions, the fastest terminates in 64s and the slowest in 69s. Looking at top, the platform utilization is indeed about 5x more in CPU%, but the concurrency does not add much to throughput. This is odd, considering that there is no synchronization requirement worth mentioning between the operations.

Following are the average times for each query in the 5 stream experiment.

Query

Virtuoso

Impala

Notes

Q1

1.95 s

191.81 s

Q2

0.70 s

40.40 s

Q3

2.01 s

95.67 s

Q4

0.71 s

345.11 s

Q5

2.93 s

112.29 s

Q6

4.76 s

14.41 s

Q7

2.08 s

329.25 s

Q8

3.00 s

98.91 s

Q9

5.58 s

250.88 s

Q10

8.23 s

55.23 s

Q11

4.26 s

27.84 s

Q12

1.74 s

37.66 s

Q13

6.07 s

147.69 s

Q14

1.73 s

23.91 s

Q15

2.27 s

23.79 s

Q16

2.41 s

34.76 s

Q17

3.92 s

362.43 s

Q18

3.02 s

348.08 s

Q19

2.27 s

443.94 s

Q20

3.05 s

92.50 s

Q21

2.00 s

623.69 s

Q22

0.37 s

61.36 s

Total for Slowest Stream

67 s

3740 s

There are 4 queries in Impala that terminated with an error (memory limit exceeded). These were two Q21s, one Q19, one Q4. One stream executed without errors, so this stream is reported as the slowest stream. Q21 will, in the absence of indexed access, do a hash build side of half of lineitem, which explains running out of memory. Virtuoso does Q21 mostly by index.

Looking at the 5 streams, we see CPU between 1000% and 2000% on either box. This looks about 5x more than the 250% per box that we were seeing with, for instance, Q1. The process sizes for impalad are over 160G, certainly enough to have the working set in memory. iostat also does not show any I, so we seem to be running from memory, as intended.

We observe that Impala does not store tables in any specific order. Therefore a merge join of orders and lineitem is not possible. Thus we always get a hash join with a potentially large build side, e.g., half of orders and half of lineitem in Q21, and all orders in Q9. This explains in part why these take so long. TPC-DS does not pose this particular problem though, as there are no tables in the DS schema where the primary key of one would be the prefix of that of another.

However, the lineitem/orders join does not explain the scores on Q1, Q20, or Q19. A simple hash join of lineitem and part was about 90s, with a replicated part hash table. In the profile, the hash probe was 74s, which seems excessive. One would have to single-step through the hash probe to find out what actually happens. Maybe there are prohibitive numbers of collisions, which would throw off the results across the board. We would have to ask the Impala community about this.

Anyway, Impala experts out there are invited to set the record straight. We have attached the results and the output of the Impala profile statement for each query for the single stream run. impala_stream0.zip contains the evidence for the single-stream run; impala-stream1-5.zip holds the 5-stream run.

To be more Big Data-like, we should probably run with significantly larger data than memory; for example, 3T in 0.5T RAM. At EC2, we could do this with 2 I3.8 instances (6.4T SSD each). With Virtuoso, we'd be done in 8 hours or so, counting 2x for the I/O and 30x for the greater scale (the 100G experiment goes in 8 minutes or so, all included). With Impala, we could be running for weeks, so at the very least we'd like to do this with an Impala expert, to make sure things are done right and will not have to be retried. Some of the hash joins would have to be done in multiple passes and with partitioning.

In subsequent articles, we will look at other players in this space, and possibly some other benchmarks, like the TPC-DS subset that Actian uses to beat Impala.

This article discusses the relationship between vectored execution and column- and row-wise data representations. Column stores are traditionally considered to be good for big scans but poor at indexed access. This is not necessarily so, though. We take TPC-H Q9 as a starting point, working with different row- and column-wise data representations and index choices. The goal of the article is to provide a primer on the performance implications of different physical designs.

All the experiments are against the TPC-H 100G dataset hosted in Virtuoso on the test system used before in the TPC-H series: dual Xeon E5-2630, 2x6 cores x 2 threads, 2.3GHz, 192 GB RAM. The Virtuoso version corresponds to the feature/analytics branch in the v7fasttrack github project. All run times are from memory, and queries generally run at full platform, 24 concurrent threads.

We note that RDF stores and graph databases usually do not have secondary indices with multiple key parts. However, these do predominantly index-based access as opposed to big scans and hash joins. To explore the impact of this, we have decomposed the tables into projections with a single dependent column, which approximates a triple store or a vertically-decomposed graph database like Sparksee.

So, in these experiments, we store the relevant data four times over, as follows:

100G TPC-H dataset in the column-wise schema as discussed in the TPC-H series, now complemented with indices on l_partkey and on l_partkey, l_suppkey

The same in row-wise data representation

Column-wise tables with a single dependent column for l_partkey, l_suppkey, l_extendedprice, l_quantity, l_discount, ps_supplycost, s_nationkey, p_name. These all have the original tables primary key, e.g., l_orderkey, l_linenumber for the l_ prefixed tables

The same with row-wise tables

The column-wise structures are in the DB qualifier, and the row-wise are in the R qualifier. There is a summary of space consumption at the end of the article. This is relevant for scalability, since even if row-wise structures can be faster for scattered random access, they will fit less data in RAM, typically 2 to 3x less. Thus, if "faster" rows cause the working set not to fit, "slower" columns will still win.

As a starting point, we know that the best Q9 is the one in the Virtuoso TPC-H implementation which is described in Part 10 of the TPC-H blog series. This is a scan of lineitem with a selective hash join followed ordered index access of orders, then hash joins against the smaller tables. There are special tricks to keep the hash tables small by propagating restrictions from the probe side to the build side.

The query texts are available here, along with the table declarations and scripts for populating the single-column projections. rs.sql makes the tables and indices, rsload.sql copies the data from the TPC-H tables.

The business question is to calculate the profit from sale of selected parts grouped by year and country of the supplier. This touches most of the tables, aggregates over 1/17 of all sales, and touches at least every page of the tables concerned, if not every row.

These are done against row- and column-wise data representations with 3 different vectorization settings. The dynamic vector size starts at 10,000 values in a vector, and adaptively upgrades this to 1,000,000 if it finds that index access is too sparse. Accessing rows close to each other is more efficient than widely scattered rows in vectored index access, so using a larger vector will likely cause a denser, hence more efficient, access pattern.

The 10K vector size corresponds to running with a fixed vector size. The Vector 1 sets vector size to 1, effectively running a tuple at a time, which corresponds to a non-vectorized engine.

We note that lineitem and its single column projections contain 600M rows. So, a vector of 10K values will hit, on the average, every 60,000th row. A vector of 1,000,000 will thus hit every 600th. This is when doing random lookups that are in no specific order, e.g., getting lineitems by a secondary index on l_partkey.

1 — Hash-based plan

Vector

Dynamic

10k

1

Column-wise

4.1 s

4.1 s

145 s

Row-wise

25.6 s

25.9 s

45.4 s

Dynamic vector size has no effect here, as there is no indexed access that would gain from more locality. The column store is much faster because of less memory access (just scan the l_partkey column, and filter this with a Bloom filter; and then hash table lookup to pick only items with the desired part). The other columns are accessed only for the matching rows. The hash lookup is vectored since there are hundreds of compressed l_partkey values available at each time. The row store does the hash lookup row by row, hence losing cache locality and instruction-level parallelism.

Without vectorization, we have a situation where the lineitem scan emits one row at a time. Restarting the scan with the column store takes much longer, since 5 buffers have to be located and pinned instead of one for the row store. The row store is thus slowed down less, but it too suffers almost a factor of 2 from interpretation overhead.

2 — Index-based, lineitem indexed on l_partkey

Vector

Dynamic

10k

1

Column-wise

30.4 s

62.3 s

321 s

Row-wise

31.8 s

27.7 s

122 s

Here the plan scans part, then partsupp, which shares ordering with part; both are ordered on partkey. Then lineitem is fetched by a secondary index on l_partkey. This produces l_orderkey, l_lineitem, which are used to get the l_suppkey. We then check if the l_suppkey matches the ps_suppkey from partsupp, which drops 3/4 of the rows. The next join is on orders, which shares ordering with lineitem; both are ordered on orderkey.

There is a narrow win for columns with dynamic vector size. When access becomes scattered, rows win by 2.5x, because there is only one page to access instead of 1 + 3 for columns. This is compensated for if the next item is found on the same page, which happens if the access pattern is denser.

3 — Index-based, lineitem indexed on L_partkey, l_suppkey

Vector

Dynamic

10k

1

Column-wise

16.9 s

47.2 s

151 s

Row-wise

22.4 s

20.7 s

89 s

This is similar to the previous, except that now only lineitems that match ps_partkey, ps_suppkey are accessed, as the secondary index has two columns. Access is more local. Columns thus win more with dynamic vector size.

4 — Decomposed, index on l_partkey

Vector

Dynamic

10k

1

Column-wise

35.7 s

170 s

601 s

Row-wise

44.5 s

56.2 s

130 s

Now, each of the l_extendedprice, l_discount, l_quantity and l_suppkey is a separate index lookup. The times are slightly higher but the dynamic is the same.

The non-vectored columns case is hit the hardest.

5 — Decomposed, index on l_partkey, l_suppkey

Vector

Dynamic

10k

1

Column-wise

19.6 s

111 s

257 s

Row-wise

32.0 s

37 s

74.9 s

Again, we see the same dynamic as with a multicolumn table. Columns win slightly more at long vector sizes because of overall better index performance in the presence of locality.

Space Utilization

The following tables list the space consumption in megabytes of allocated pages. Unallocated space in database files is not counted.

The row-wise table also contains entries for column-wise structures (DB.*) since these have a row-wise sparse index. The size of this is however negligible, under 1% of the column-wise structures.

Row-Wise

Column-Wise

MB

structure

73515

R.DBA.LINEITEM

14768

R.DBA.ORDERS

11728

R.DBA.PARTSUPP

10161

r_lpk_pk

10003

r_l_pksk

9908

R.DBA.l_partkey

8761

R.DBA.l_extendedprice

8745

R.DBA.l_discount

8738

r_l_pk

8713

R.DBA.l_suppkey

6267

R.DBA.l_quantity

2223

R.DBA.CUSTOMER

2180

R.DBA.o_orderdate

2041

r_O_CK

1911

R.DBA.PART

1281

R.DBA.ps_supplycost

811

R.DBA.p_name

127

R.DBA.SUPPLIER

88

DB.DBA.LINEITEM

24

DB.DBA.ORDERS

11

DB.DBA.PARTSUPP

9

R.DBA.s_nationkey

5

l_pksk

4

DB.DBA.l_partkey

4

lpk_pk

4

DB.DBA.l_extendedprice

3

l_pk

3

DB.DBA.l_suppkey

2

DB.DBA.CUSTOMER

2

DB.DBA.l_quantity

1

DB.DBA.PART

1

O_CK

1

DB.DBA.l_discount

MB

structure

36482

DB.DBA.LINEITEM

13087

DB.DBA.ORDERS

11587

DB.DBA.PARTSUPP

5181

DB.DBA.l_extendedprice

4431

l_pksk

3072

DB.DBA.l_partkey

2958

lpk_pk

2918

l_pk

2835

DB.DBA.l_suppkey

2067

DB.DBA.CUSTOMER

1618

DB.DBA.PART

1156

DB.DBA.l_quantity

961

DB.DBA.ps_supplycost

814

O_CK

798

DB.DBA.l_discount

724

DB.DBA.p_name

436

DB.DBA.o_orderdate

126

DB.DBA.SUPPLIER

1

DB.DBA.s_nationkey

In both cases, the large tables are on top, but the column-wise case takes only half the space due to compression.

We note that the single column projections are smaller column-wise. The l_extendedprice is not very compressible hence column-wise takes much more space than l_quantity; the row-wise difference is less. Since the leading key parts l_orderkey, l_linenumber are ordered and very compressible, the column-wise structures are in all cases noticeably more compact.

The same applies to the multipart index l_pksk and r_l_pksk (l_partkey, l_suppkey, l_orderkey, l_linenumber) in column- and row-wise representations.

Note that STRING columns (e.g., l_comment) are not compressed. If they were, the overall space ratio would be even more to the advantage of the column store.

Conclusions

Column stores and vectorization inextricably belong together. Column-wise compression yields great gains also for indices, since sorted data is easy to compress. Also for non-sorted data, adaptive use of dictionaries, run lengths, etc., produce great space savings. Columns also win with indexed access if there is locality.

Row stores have less dependence on locality, but they also will win by a factor of 3 from dropping interpretation overhead and exploiting join locality.

For point lookups, columns lose by 2+x but considering their better space efficiency, they will still win if space savings prevent going to secondary storage. For bulk random access, like in graph analytics, columns will win because of being able to operate on a large vector of keys to fetch.

For many workloads, from TPC-H to LDBC social network, multi-part keys are a necessary component of physical design for performance if indexed access predominates. Triple stores and most graph databases do not have such and are therefore at a disadvantage. Self-joining, like in RDF or other vertically decomposed structures, can cost up to a factor of 10-20 over a column-wise multicolumn table. This depends however on the density of access.

For analytical workloads, where the dominant join pattern is the scan with selective hash join, column stores are unbeatable, as per common wisdom. There are good physical reasons for this and the row store even with well implemented vectorization loses by a factor of 5.

For decomposed structures, like RDF quads or single column projections of tables, column stores are relatively more advantageous because the key columns are extensively repeated, and these compress better with columns than with rows. In all the RDF workloads we have tried, columns never lose, but there is often a draw between rows and columns for lookup workloads. The longer the query, the more columns win.

In this series, we will look at Virtuoso and some of the big data technologies out there. SQL on Hadoop is of interest, as well as NoSQL technologies.

We begin at the beginning, with Hive, the grand-daddy of SQL on Hadoop.

The test platform is two Amazon R3.8 AMI instances. We compared Hive with the Virtuoso 100G TPC-H experiment on the same platform, published earlier on this blog. The runs follow a bulk load in both cases, with all data served from memory. The platform has 2x244GB RAM with only 40GB or so of working set.

Load time with Hive was 742 seconds against 232 seconds with Virtuoso. In both cases, this was a copy from 32 CSV files into native database format; for Hive, this is ORC (Optimized Row Columnar). In Virtuoso, there is one index, (o_custkey); in Hive, there are no indices.

Query

Virtuoso

Hive

Notes

—

332 s

742 s

Data Load

Q1

1.098 s

296.636 s

Q2

0.187 s

>3600 s

Hive Timeout

Q3

0.761 s

98.652 s

Q4

0.205 s

147.867 s

Q5

0.808 s

114.782 s

Q6

2.403 s

71.789 s

Q7

0.59 s

394.201 s

Q8

0.775 s

>3600 s

Hive Timeout

Q9

1.836 s

>3600 s

Hive Timeout

Q10

3.165 s

179.646 s

Q11

1.37 s

43.094 s

Q12

0.356 s

101.193 s

Q13

2.233 s

208.476 s

Q14

0.488 s

89.047 s

Q15

0.72 s

136.431 s

Q16

0.814 s

105.652 s

Q17

0.681 s

255.848 s

Q18

1.324 s

337.921 s

Q19

0.417 s

>3600 s

Hive Timeout

Q20

0.792 s

193.965 s

Q21

0.720 s

670.718 s

Q22

0.155 s

68.462 s

Hive does relatively best on bulk load. This is understandable since this is a sequential read of many files in parallel with just compression to do.

Hive's query times are obviously affected by not having a persistent memory image of the data, as this is always streamed from the storage files into other files as MapReduce intermediate results. This seems to be an operator-at-a-time business as opposed to Virtuoso's vectorized streaming.

The queries that would do partitioned hash joins (e.g., Q9) did not finish under an hour in Hive, so we do not have a good metric of a cross-partition hash join.

One could argue that one should benchmark Hive only in disk-bound circumstances. We may yet get to this.

Our next stop will probably be Impala, which ought to do much better than Hive, as it dose not have the MapReduce overheads.

If you are a Hive expert and believe that Hive should have done much better, please let us know how to improve the Hive scores, and we will retry.

I will here take a break from core database and talk a bit about EU policies for research funding.

I had lunch with Stefan Manegold of CWI last week, where we talked about where European research should go. Stefan is involved in RETHINK big, a European research project for compiling policy advice regarding big data for EC funding agencies. As part of this, he is interviewing various stakeholders such as end user organizations and developers of technology.

RETHINK big wants to come up with a research agenda primarily for hardware, anything from faster networks to greener data centers. CWI represents software expertise in the consortium.

So, we went through a regular questionnaire about how we see the landscape. I will summarize this below, as this is anyway informative.

Core competence

My own core competence is in core database functionality, specifically in high performance query processing, scale-out, and managing schema-less data. Most of the Virtuoso installed base is in the RDF space, but most potential applications are in fact outside of this niche.

User challenges

The life sciences vertical is the one in which I have the most application insight, from going to Open PHACTS meetings and holding extensive conversations with domain specialists. We have users in many other verticals, from manufacturing to financial services, but there I do not have as much exposure to the actual applications.

Having said this, the challenges throughout tend to be in diversity of data. Every researcher has their MySQL database or spreadsheet, and there may not even be a top level catalogue of everything. Data formats are diverse. Some people use linked data (most commonly RDF) as a top level metadata format. The application data, such as gene sequences or microarray assays, reside in their native file formats and there is little point in RDF-izing these.

There are also public data resources that are published in RDF serializations as vendor-neutral, self-describing format. Having everything as triples, without a priori schema, makes things easier to integrate and in some cases easier to describe and query.

So, the challenge is in the labor intensive nature of data integration. Data comes with different levels of quantity and quality, from hand-curated to NLP extractions. Querying in the single- or double-digit terabyte range with RDF is quite possible, as we have shown many times on this blog, but most use cases do not even go that far. Anyway, what we see on the field is primarily a data diversity game. The scenario is data integration; the technology we provide is database. The data transformation proper, data cleansing, units of measure, entity de-duplication, and such core data-integration functions are performed using diverse, user-specific means.

Jerven Bolleman of the Swiss Institute of Bioinformatics is a user of ours with whom we have long standing discussions on the virtues of federated data and querying. I advised Stefan to go talk to him; he has fresh views about the volume challenges with unexpected usage patterns. Designing for performance is tough if the usage pattern is out of the blue, like correlating air humidity on the day of measurement with the presence of some genomic patterns. Building a warehouse just for that might not be the preferred choice, so the problem field is not exhausted. Generally, I’d go for warehousing though.

What technology would you like to have? Network or power efficiency?

OK. Even a fast network is a network. A set of processes on a single shared-memory box is also a kind of network. InfiniBand is maybe half the throughput and 3x the latency of single threaded interprocess communication within one box. The operative word is latency. Making large systems always involves a network or something very much like one in large scale-up scenarios.

On the software side, next to nobody understands latency and contention; yet these are the one core factor in any pursuit of scalability. Because of this situation, paradigms like MapReduce and bulk synchronous parallel (BSP) processing have become popular because these take the communication out of the program flow, so the programmer cannot muck this up, as otherwise would happen with the inevitability of destiny. Of course, our beloved SQL or declarative query in general does give scalability in many tasks without programmer participation. Datalog has also been used as a means of shipping computation around, as in the the work of Hellerstein.

There are no easy solutions. We have built scale-out conscious, vectorized extensions to SQL procedures where one can express complex parallel, distributed flows, but people do not use or understand these. These are very useful, even indispensable, but only on the inside, not as a programmer-facing construct. MapReduce and BSP are the limit of what a development culture will absorb. MapReduce and BSP do not hide the fact of distributed processing. What about things that do? Parallel, partitioned extensions to Fortranarrays? Functional languages? I think that all the obvious aids to parallel/distributed programming have been conceived of. No silver bullet; just hard work. And above all the discernment of what paradigm fits what problem. Since these are always changing, there is no finite set of rules, and no substitute for understanding and insight, and the latter are vanishingly scarce. "Paradigmatism," i.e., the belief that one particular programming model is a panacea outside of its original niche, is a common source of complexity and inefficiency. This is a common form of enthusiastic naïveté.

If you look at power efficiency, the clusters that are the easiest to program consist of relatively few high power machines and a fast network. A typical node size is 16+ cores and 256G or more RAM. Amazon has these in entirely workable configurations, as documented earlier on this blog. The leading edge in power efficiency is in larger number of smaller units, which makes life again harder. This exacerbates latency and forces one to partition the data more often, whereas one can play with replication of key parts of data more freely if the node size is larger.

One very specific item where research might help without having to rebuild the hardware stack would be better, lower-latency exposure of networks to software. Lightweight threads and user-space access, bypassing slow protocol stacks, etc. MPI has some of this, but maybe more could be done.

So, I will take a cluster of such 16-core, 256GB machines on a faster network, over a cluster of 1024 x 4G mobile phones connected via USB. Very selfish and unecological, but one has to stay alive and life is tough enough as is.

Are there pressures to adapt business models based on big data?

The transition from capex to opex may be approaching maturity, as there have been workable cloud configurations for the past couple of years. The EC2 from way back, with at best a 4 core 16G VM and a horrible network for $2/hr, is long gone. It remains the case that 4 months of 24x7 rent in the cloud equals the purchase price of physical hardware. So, for this to be economical long-term at scale, the average utilization should be about 10% of the peak, and peaks should not be on for more than 10% of the time.

So, database software should be rented by the hour. A 100-150% markup for the $2.80 a large EC2 instance costs would be reasonable. Consider that 70% of the cost in TPC benchmarks is database software.

There will be different pricing models combining different up-front and per-usage costs, just as there are for clouds now. If the platform business goes that way and the market accepts this, then systems software will follow. Price/performance quotes should probably be expressed as speed/price/hour instead of speed/price.

The above is rather uncontroversial but there is no harm restating these facts. Reinforce often.

Well, the question is raised, what should Europe do that would have tangible impact in the next 5 years?

This is a harder question. There is some European business in wide area and mobile infrastructures. Competing against Huawei will keep them busy. Intel and Mellanox will continue making faster networks regardless of European policies. Intel will continue building denser compute nodes, e.g., integrated Knight’s Corner with dual IB network and 16G fast RAM on chip. Clouds will continue making these available on demand once the technology is in mass production.

What’s the next big innovation? Neuromorphic computing? Quantum computing? Maybe. For now, I’d just do more engineering along the core competence discussed above, with emphasis on good marketing and scalable execution. By this I mean trained people who know something about deployment. There is a huge training gap. In the would-be "Age of Data," knowledge of how things actually work and scale is near-absent. I have offered to do some courses on this to partners and public alike, but I need somebody to drive this show; I have other things to do.

I have been to many, many project review meetings, mostly as a project partner but also as reviewer. For the past year, the EC has used an innovation questionnaire at the end of the meetings. It is quite vague, and I don’t think it delivers much actionable intelligence.

What would deliver this would be a venture capital type activity, with well-developed networks and active participation in developing a business. The EC is not now set up to perform this role, though. But the EC is a fairly large and wealthy entity, so it could invest some money via this type of channel. Also there should be higher individual incentives and rewards for speed and excellence. Getting the next Horizon 2020 research grant may be good, but better exists. The grants are competitive enough and the calls are not bad; they follow the times.

In the projects I have seen, productization does get some attention, e.g., the LOD2 stack, but it is not something that is really ongoing or with dedicated commercial backing. It may also be that there is no market to justify such dedicated backing. Much of the RDF work has been "me, too" — let’s do what the real database and data integration people do, but let’s just do this with triples. Innovation? Well, I took the best of the real DB world and adapted this to RDF, which did produce a competent piece of work with broad applicability, extending outside RDF. Is there better than this? Well, some of the data integration work (e.g., LIMES) is not bad, and it might be picked up by some of the players that do this sort of thing in the broader world, e.g., Informatica, the DI suites of big DB vendors, Tamr, etc. I would not know if this in fact adds value to the non-RDF equivalents; I do not know the field well enough, but there could be a possibility.

The recent emphasis for benchmarking, spearheaded by Stefano Bertolo is good, as exemplified by the LDBC FP7. There should probably be one or two projects of this sort going at all times. These make challenges known and are an effective means of guiding research, with a large multiplier: Once a benchmark gets adopted, infinitely more work goes into solving the problem than in stating it in the first place.

The aims and calls are good. The execution by projects is variable. For 1% of excellence, there apparently must be 99% of so-and-so, but this is just a fact of life and not specific to this context. The projects are rather diffuse. There is not a single outcome that gets all the effort. In this, the level of engagement of participants is less and focus is much more scattered than in startups. A really hungry, go-getter mood is mostly absent. I am a believer in core competence. Well, most people will agree that core competence is nice. But the projects I have seen do not drive for it hard enough.

It is hard to say exactly what kinds of incentives could be offered to encourage truly exceptional work. The American startup scene does offer high rewards and something of this could be transplanted into the EC project world. I would not know exactly what form this could take, though.

We have another new Amazon machine image, this time for deploying your own Virtuoso Elastic Cluster on the cloud. The previous post gave a summary of running TPC-H on this image. This post is about what the AMI consists of and how to set it up.

Note: This AMI is running a pre-release build of Virtuoso 7.5, Commercial Edition. Features are subject to change, and this build is not licensed for any use other than the AMI-based benchmarking described herein.

There are two preconfigured cluster setups; one is for two (2) machines/instances and one is for four (4). Generation and loading of TPC-H data, as well as the benchmark run itself, is preconfigured, so you can do it by entering just a few commands. The whole sequence of doing a terabyte (1000G) scale TPC-H takes under two hours, with 30 minutes to generate the data, 35 minutes to load, and 35 minutes to do three benchmark runs. The 100G scale is several times faster still.

Detailed instructions are on the AMI, in /home/ec2-user/cluster_instructions.txt, but the basic steps to get up and running are as follows:

Instantiate machine image ami-811becea) (AMI ID is subject to change; you should be able to find the latest by searching for "OpenLink Virtuoso Benchmarks" in "Community AMIs"; this one is short-named virtuoso-bench-cl) with two or four (2 or 4) R3.8xlarge instances within one virtual private cluster and placement group. Make sure the VPC security is set to allow all connections.

Log in to the first, and fill in the configuration file with the internal IP addresses of all machines instantiated in step 1.

Distribute the license files to the instances, and start the OpenLink License Manager on each machine.

Run 3 shell commands to set up the file systems and the Virtuoso configuration files.

If you do not plan to run one of these benchmarks, you can simply start and work with the Virtuoso cluster now. It is ready for use with an empty database.

Before running one of these benchmark, generate the appropriate dataset with the dbgen.sh command.

Bulk load the data with load.sh.

Run the benchmark with run.sh.

Right now the cluster benchmarks are limited to TPC-H but cluster versions of the LDBC Social Network and Semantic Publishing benchmarks will follow soon.

We have made an Amazon EC2 deployment of Virtuoso 7 Commercial Edition, configured to use the Elastic Cluster Module with TPC-H preconfigured, similar to the recently published OpenLink Virtuoso Benchmark AMI running the Open Source Edition. The details of the new Elastic Cluster AMI and steps to use it will be published in a forthcoming post. Here we will simply look at results of running TPC-H 100G scale on two machines, and 1000G scale on four machines. This shows how Virtuoso provides great performance on a cloud platform. The extremely fast bulk load — 33 minutes for a terabyte! — means that you can get straight to work even with on-demand infrastructure.

In the following, the Amazon instance type is R3.8xlarge, each with dual Xeon E5-2670 v2, 244G RAM, and 2 x 300G SSD. The image is made from the Amazon Linux with built-in network optimization. We first tried a RedHat image without network optimization and had considerable trouble with the interconnect. Using network-optimized Amazon Linux images inside a virtual private cloud has resolved all these problems.

The network optimized 10GE interconnect at Amazon offers throughput close to the QDR InfiniBand running TCP-IP; thus the Amazon platform is suitable for running cluster databases. The execution that we have seen is not seriously network bound.

For the larger scale we did 3 sets of power + throughput tests to measure consistency of performance. By the TPC-H rules, the worst (first) score should be reported. Even after bulk load, this is markedly less than the next power score due to working set effects. This is seen to a lesser degree with the first throughput score also.

This package is ideal for technology evaluators and developers interested in getting the most performance out of Virtuoso. This is also an all-in-one solution to any questions about reproducing claimed benchmark results. All necessary tools for building and running are included; thus any developer can use this model installation as a starting point. The benchmark drivers are preconfigured with appropriate settings, and benchmark qualification tests can be run with a single command.

The Benchmarks AMI includes a precompiled, preconfigured checkout of the v7fasttrack github repository, checkouts of the github repositories of the benchmarks, and a number of running directories with all configuration files preset and optimized. The image is intended to be instantiated on a R3.8xlarge Amazon instance with 244G RAM, dual Xeon E5-2670 v2, and 600G SSD.

Benchmark datasets and preloaded database files can be downloaded from S3 when large, and generated as needed on the instance when small. As an alternative, the instance is also set up to do all phases of data generation and database bulk load.

The following benchmark setups are included:

TPC-H 100G

TPC-H 300G

LDBC SNB Validation

LDBC SNB Interactive 100G

LDBC SNB Interactive 300G (SF3)

LDBC SPB Validation

LDBC SPB Basic 256 Mtriples (SF5)

LDBC SPB Basic 1 Gtriple

The AMI will be expanded as new benchmarks are introduced, for example, the LDBC Social Network Business Intelligence or Graph Analytics.

To get started:

Instantiate machine image ami-eb789280 (AMI ID is subject to change; you should be able to find the latest by searching for "OpenLink Virtuoso Benchmarks" in "Community AMIs"; this one is short-named virtuoso-bench-6) with a R3.8xlarge instance.

Connect via ssh.

See the README (also found in the ec2-user's home directory) for full instructions on getting up and running.